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Classification of air quality monitoring stations using fuzzy similarity measures: A case study
Center for Environmental Science and Engineering (CESE), Indian Institute of Technology, Bombay, India.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Center for Environmental Science and Engineering (CESE), Indian Institute of Technology, Bombay, India.
University of California, Berkeley, United States.
2016 (English)In: Studies in Fuzziness and Soft Computing, ISSN 1434-9922, E-ISSN 1860-0808, Vol. 342, 489-501 p.Article in journal (Refereed) Published
Abstract [en]

The objective of designing and installation air quality monitoring network (AQMN) is to reduce network density with a view to acquire maximum information on air quality with minimum expenses. In spite of the best research efforts, there has been no general acceptance of any method for deciding the number of stations. Majority of the cities have, therefore, installed monitoring stations with their own guidelines. The present paper presents a useful formulation for classification of the existing air quality monitoring stations (AQMS) using fuzzy similarity measures. The case study has been demonstrated by applying the methodology to the already-installed AQMS in Delhi, India.

Place, publisher, year, edition, pages
2016. Vol. 342, 489-501 p.
Keyword [en]
Air quality data, Air quality monitoring network, Classification, Cosine amplitude and max–min method, Fuzzy similarity measures
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:mdh:diva-33499DOI: 10.1007/978-3-319-32229-2_34ISI: 000390417100034ScopusID: 2-s2.0-84991783419OAI: oai:DiVA.org:mdh-33499DiVA: diva2:1044357
Available from: 2016-11-03 Created: 2016-11-03 Last updated: 2017-01-13Bibliographically approved

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Citation style
  • apa
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  • vancouver
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Language
  • de-DE
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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